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arXiv · 1807.01280

On the Computational Power of Online Gradient Descent

Vaggos Chatziafratis·Tim Roughgarden·Joshua R. Wang
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Abstract

We prove that the evolution of weight vectors in online gradient descent can encode arbitrary polynomial-space computations, even in very simple learning settings. Our results imply that, under weak complexity-theoretic assumptions, it is impossible to reason efficiently about the fine-grained behavior of online gradient descent.

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Vaggos Chatziafratis, Tim Roughgarden, Joshua R. Wang. 2018-07-03. On the Computational Power of Online Gradient Descent. https://arxiv.org/abs/1807.01280

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